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在AA100合金中使用ANN和ANFIS预测模型对摩擦接接的可靠性评估
J Gokulachandran1, A Sumesh2, M S Narassima3
1Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Scientific reports
|September 26, 2025
概括
这项研究优化了对AA 1100合金的摩擦冲动接 (FSW). 适应性神经模糊推理系统 (ANFIS) 有效地预测并提高了接可靠性,实现了优越关节强度的最佳参数.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 计算智能是一种计算智能.
背景情况:
- 摩擦接 (FSW) 是一种对合金至关重要的固态连接技术.
- 优化FSW参数对于最大限度地提高接质量和可靠性至关重要.
- 预测接可靠性的传统方法可能是复杂和耗时的.
研究的目的:
- 调查FSW参数 (螺旋转速,料速度,轴负荷) 对AA 1100合金的抗拉强度和可靠性的影响.
- 开发和比较软计算模型,特别是自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN),用于预测接可靠性.
- 确定最佳的FSW参数,以实现AA 1100合金中最大的关节可靠性.
主要方法:
- 利用Taguchi L27直角阵列进行高效的实验设计.
- 进行了拉伸试验,以评估接强度和确定故障时间.
- 应用信号与噪声 (S/N) 比率分析,以确定影响的关键参数.
- 使用韦布尔分析来评估接的可靠性.
- 开发了ANFIS和ANN模型,根据工艺参数预测接可靠性.
主要成果:
- ANFIS模型显著优于ANN模型,显示较低的平均百分比误差 (4.61%与19.95%) 和根平均平方误差 (RMSE) (0.002与0.008).
- 确定了最大限度地提高关节可靠性的最佳FSW参数:1200rpm旋转转速,18mm/min料速率和7kN轴负.
- 在最佳参数设置下,实现了0.87的预测接可靠性.
结论:
- 安菲斯是预测和优化AA 1100合金中FSW接头可靠性的高效工具.
- 已确定的最佳FSW参数为生产高质量的接提供了一种可靠的方法.
- 这项研究有助于推进固态接的智能制造和质量控制.
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